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Yiwei Sun†,§, Ngot Bui‡, Tsung-Yu Hsieh†,§ and Vasant Honavar†,∗Research Paper • September 19th, 2018
Abstract—Real-world social networks and digital platforms are comprised of individuals (nodes) that are linked to other indi- viduals or entities through multiple types of relationships (links). Sub-networks of such a network based on each type of link correspond to distinct views of the underlying network. In real- world applications each node is typically linked to only a small subset of other nodes. Hence, practical approaches to problems such as node labeling have to cope with the resulting sparse networks. While low-dimensional network embeddings offer a promising approach to this problem, most of the current network embedding methods focus primarily on single view networks. We introduce a novel multi-view network embedding (MVNE) algorithm for constructing low-dimensional node embeddings from multi-view networks. MVNE adapts and extends an ap- proach to single view node embedding using graph factorization clustering (GFC) to the multi-view setting using an objective function that
